Procedures for Adjusting Regional Regression Models of Urban-Runoff Quality Using Local Data
GEOLOGICAL SURVEY WASHINGTON DC
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Statistical operations termed model-adjustment procedures MAPs can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting adjusted regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAPs examined in this study were single-factor regression against the regional model prediction, Pu, termed MAP-lF-P, regression against Pu, termed MAP-R-P, regression against P, and additional local variables termed MAP-R-PnV, and a weighted combination of Pu and a local-regression prediction termed MAP-W.
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